KSU: Drivers of Fungal Community Dynamics

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Prep data

0.1 relabel ONF as North

0.2 load pam res

0.3 leaflet metadata

0.4 leaflet maps

Set up Ordinations/NMDS

0.5 Check Gap Statistic on NMDS

0.6 ordinate

definitely a subset of samples with diss = 1 / lack of shared features. to investigate later?

1 Map of sites and samples

Prior mislabeling of samples from ONF

The Kansas site ONF was labeled as ‘South Middle’ in factor ‘Bin’ in previous manuscripts. It should be ‘North’.

Map of sites colored by latitudinal bin.

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2 Overview of Site clusters (NMDS by Site)

I wanted to see if samples grouped by geographic location, and given that sites range widely in distance from one another, I decided to visually assess using a color scheme that at least roughly represented their location in 2D space. See the map below:

In the NMDS (Sørensen), we see definite clustering by Site, but not all groupings are distinct.

We also see evidence of potentially significant spatial relationships (i.e., distance-decay).

Fun

All in 1 plot

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Facet by Lat bin

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Facet by Long bin

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Lat:Long

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3 More in-depth: distance-based clustering

Background

Microbiome sample clustering can be performed using either model-based methods and machine learning methods.
- Machine learning methods, which rely on defined distance metrics, are used more frequently than model-based statistical methods (“due to their efficient implementation and easy interpretation.”)
- I used the partition around medoids (PAM) clustering method, which is related to but considered more robust than K-means. In contrast to K-means, which can be sensitive to the effects of outliers, PAM’s optimization goal is to minimize the sum of distances to the medoids instead of minimizing the sum of the squared distances to the cluster centers.

Note: clustering was performed directly on distance matrices, not ordinations or ordination scores

3.1 Gap Statistic (on distance matrix)

view gap on bray

view gap on sørensen

3.2 perform PAM

# Perform PAM clustering and save - Fun
pam_fwc_sor_k10 <- pam(Fun_wc_sorensen_distmat, k = 10, diss = T, cluster.only = T) 

saveRDS(pam_fwc_sor_k10, file = "../processed_data/clean_rds_saves/pam/pam_fwc_sor_k10.rds")


# Perform PAM clustering and save - Bac
pam_bwc_sor_k14 <- pam(Bac_wc_sorensen_distmat, k = 14, diss = T, cluster.only = T) 
pam_bwc_sor_k14<-readRDS(file = "../processed_data/clean_rds_saves/pam/pam_bwc_sor_k14.rds")
saveRDS(pam_bwc_sor_k14, file = "../processed_data/clean_rds_saves/pam/pam_bwc_sor_k14.rds")

# add bac pam to bac sd
sample_data(Bac_wholecommunity)$bac_clus_sor_k10_og <- pam_bwc_sor_k14
sample_data(Bac_wholecommunity)$bac_clus_sor_k10_og <- as.factor(sample_data(Bac_wholecommunity)$bac_clus_sor_k10_og)

# add fun pam to bac sd
sample_data(Bac_wholecommunity)$Fun_sor_clusters <- sample_data(Fun_wholecommunity)$Fun_sor_clusters
sample_data(Bac_wholecommunity)$Fun_sor_clusters <- as.factor(sample_data(Bac_wholecommunity)$Fun_sor_clusters)

saveRDS(Bac_wholecommunity,file="../processed_data/clean_rds_saves/Bac_wholecommunity.rds")

# tail(colnames(sample_data(Bac_wholecommunity)))
# tail(sample_data(Fun_wholecommunity)[1:8])

# add bac pam to fun sd
sample_data(Fun_wholecommunity)$bac_clus_sor_k10_og <- as.factor(sample_data(Bac_wholecommunity)$bac_clus_sor_k10_og)
tail(colnames(sample_data(Fun_wholecommunity)))

saveRDS(Fun_wholecommunity,file="../processed_data/clean_rds_saves/Fun_wholecommunity.rds")

3.3 view Sørensen clusters on NMDS

Note the density of cluster 1 - I’ll investigate that further.

Fun, k = 10

All samples

Facet by Lat

Facet by Long

Facet by Cluster

We do see clusters with only 1 site and others with multiple sites.

Fungal cluster descriptions

Fungal cluster descriptions

Clusters not yet relabled here…

Descriptions of Sørensen distance-based clusters. †variables listed are significant in all vs. base-mean Wilcox test with BH p-value corrections. * p-value < 0.05, ** < 0.001, *** < 0.0001, **** < 1e-5.

Cluster	total n	Site(s)	Grass(es)	Characteristics†	Exclusivity
1	183			spans pH range
2	37	BNP,DMT,FMT,SEV	BOER (n=33), BOGR (n=4)	high pH* (6.8 - 8.3, mean 7.5)
3	56			high pH**** (6.8 - 8.3, mean 7.8)
4	17			lower pH**** (5.6 - 7.6, mean 6.2)
5	43			high pH** (7.1 - 7.8, mean 7.6)
6	32			lower pH**** (5.1 - 7.8, mean 6.2)
7	38
8	29	mostly LAR (n=27)		high pH**** (7.2 - 8.2, mean 8.0)
9	9	SFA	SCSC (only grass present)		Site=SFA
10	29	KAE		lower pH**** (5.9 - 7.2, mean 6.3)	Site=KAE (of the 32 KAE samples, only 3 others were in diff clusters)

Bac, k = 14

All samples

Facet by Lat

Facet by Long

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3.4 Are the clusters distinct/distinguishable? (randomForest)

Clusters: Sørensen dissimilarity clusters based on pam (k = 10)

Method: R package randomForest v4.7.1.1

predictors.all<-t(otu_table(Fun_wholecommunity))

response.Fun_sor_clusters<-as.factor(sample_data(Fun_wholecommunity)$Fun_sor_clusters)

rf.data.Fun_sor_clusters<-data.frame(response.Fun_sor_clusters, predictors.all)

classify.Fun_sor_clusters<-randomForest(response.Fun_sor_clusters~., data = rf.data.Fun_sor_clusters, ntree=999)

Call: randomForest(formula = response.Fun_sor_clusters ~ ., data = rf.data.Fun_sor_clusters, ntree = 999)

Type of random forest: classification

Number of trees: 999

No. of variables tried at each split: 81

OOB estimate of error rate: 6.82%

Confusion matrix of fungal whole community randomForest classification based on Sørensen-based PAM clusters. Diagonals are accurate calls. Misclassifications are shown as values by row, i.e., cluster 2 was misclassified as cluster 3 in 3 samples, while 34 samples were accurately called, resulting in an error rate of 8.1% for that cluster.

	1	2	3	4	5	6	7	8	9	10	Cluster error %
F1	183										0.0
F2		34	3								8.1
F3		5	50		1						10.7
F4				12	4		1				29.4
F5			1		50	1	1				5.7
F6					7	23	2				28.1
F7			1			2	34			1	10.5
F8			1					27		1	6.9
F9									10		0
F10					1					28	3.4

Notes: OTU205 is highest in relative abundance in Cluster 1, most other clusters have minimal or no presence. Exception is sample in Cluster 7, which was the one misclassified into Cluster 1.

OTU6679, Generalist.test: all (global RA = 1.65%) v. Cluster 1 (mean RA = 3.75%)

3.5 Dendrogram of Sørensen-based clusters (k = 10)

Fungal whole community

Dendrogram of Sørensen-based clusters of fungal whole communities. The terminal segments hang to within-cluster dissimilarity.

Clusters 2 & 3 seem to be the outgroup / most distantly related
Clusters 1 & 9 are very similar, followed by 10 -> wrap-around cluster numbering

Which OTUs are driving the differences between clusters 1 & 2?

3.5.1 SIMPER results

not updated yet

average - Species contribution to average between-group dissimilarity
cusum - Ordered cumulative contribution (to between-group dissimilarity). These are based on item average, but they sum up to total 1
p - Permutation \(p\)-value. Probability of getting a larger or equal average contribution in random permutation of the group factor

Mean Sørensen dissimilarity between Clusters 1 & 2: 0.920 (unweighted) / 0.903 (weighted by sample size)

Mean within-cluster similarities
- Cluster 1: 0.514
- Cluster 2: 0.802

3.6 Mapping metadata

3.6.1 Edaphic

pH

soil moisture

SOM

ammonium

phosphorous

3.6.2 Climate

Precipitation

ppt3yr = mean annual precipitation (determined over a 3-year window)

Growing degree days

GDD3yr = growing degree days (determined over a 3-year window)

3.6.3 Plant Traits

Specific root length

avg_SRL = specific root length

Specific leaf area

avg_SLA = specific leaf area

Herbivory percent

herbivory_perc = average site level damage estimate for herbivory, averaged over all species and individuals at the site; could be used to indicate herbivory pressure at the site level - ranges from 1:17 (%)

3.6.4 Factors

Longitude (longitudinal ‘gradients’)

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in progress

next: - bioenv

3.7 all?

# no facet

plot_ordination(Fun_wholecommunity, Fun_wholecommunity.k3.ord, color = "Gradient") +
  geom_text(mapping = aes(label = Site_Grass), size = 4, vjust = 1.5) +
  scale_color_manual(values = fun_grad_cols_n3
                     # ,name="Grass Host"
                     ) +
  geom_point(size = 3, alpha = 0.9) +
  labs(y="") +
  ggtitle("", subtitle = "ITS - k=4") +
  theme_bw() +
  theme(legend.position="right",
    axis.text.x = element_text(color="black", size=12),
    axis.text.y = element_text(color="black", size=12),
    axis.title.x = element_text(color="black", size=14),
    axis.title.y = element_text(color="black", size=14),
    plot.background = element_blank(),panel.border = element_rect(colour = "black", fill=NA, linewidth=.75),
    panel.grid.major = element_blank(),panel.grid.minor = element_blank(),panel.background = element_blank())

plot_ordination(Fun_wholecommunity, Fun_wholecommunity.k3.ord, color = "Bin") +
  geom_text(mapping = aes(label = Site_Grass), size = 4, vjust = 1.5) +
  scale_color_manual(values = fun_bin_cols_n5
                     # ,name="Grass Host"
                     ) +
  geom_point(size = 3, alpha = 0.9) +
  labs(y="") +
  ggtitle("", subtitle = "ITS - k=4") +
  theme_bw() +
  theme(legend.position="right",
    axis.text.x = element_text(color="black", size=12),
    axis.text.y = element_text(color="black", size=12),
    axis.title.x = element_text(color="black", size=14),
    axis.title.y = element_text(color="black", size=14),
    plot.background = element_blank(),panel.border = element_rect(colour = "black", fill=NA, linewidth=.75),
    panel.grid.major = element_blank(),panel.grid.minor = element_blank(),panel.background = element_blank())

plot_ordination(Fun_wholecommunity, Fun_wholecommunity.k3.ord, color = "Grass") +
  geom_text(mapping = aes(label = Site_Grass), size = 4, vjust = 1.5) +
  scale_color_manual(values = fun_grass_cols
                     # ,name="Grass Host"
                     ) +
  geom_point(size = 3, alpha = 0.9) +
  labs(y="") +
  ggtitle("", subtitle = "ITS - k=4") +
  theme_bw() +
  theme(legend.position="right",
    axis.text.x = element_text(color="black", size=12),
    axis.text.y = element_text(color="black", size=12),
    axis.title.x = element_text(color="black", size=14),
    axis.title.y = element_text(color="black", size=14),
    plot.background = element_blank(),panel.border = element_rect(colour = "black", fill=NA, linewidth=.75),
    panel.grid.major = element_blank(),panel.grid.minor = element_blank(),panel.background = element_blank())

# facet Grass
plot_ordination(Fun_wholecommunity, Fun_wholecommunity.k3.ord, color = "Gradient") +
  geom_text(mapping = aes(label = Site_Grass), size = 4.5, vjust = 1.5) +
  scale_color_manual(values = fun_grad_cols_n3
                     # ,name="Grass Host"
                     ) +
  geom_point(size = 3, alpha = 0.9) +
  facet_wrap(.~Grass, scales = "free") +
  labs(y="") +
  ggtitle("", subtitle = "ITS - k=4") +
  theme_bw() +
  theme(legend.position="right",
    axis.text.x = element_text(color="black", size=12),
    axis.text.y = element_text(color="black", size=12),
    axis.title.x = element_text(color="black", size=14),
    axis.title.y = element_text(color="black", size=14),
    plot.background = element_blank(),panel.border = element_rect(colour = "black", fill=NA, linewidth=.75),
    panel.grid.major = element_blank(),panel.grid.minor = element_blank(),panel.background = element_blank())

plot_ordination(Fun_wholecommunity, Fun_wholecommunity.k3.ord, color = "Bin") +
  geom_text(mapping = aes(label = Site_Grass), size = 4.5, vjust = 1.5) +
  scale_color_manual(values = fun_bin_cols_n5
                     # ,name="Grass Host"
                     ) +
  geom_point(size = 4, alpha = 0.9) +
  # scale_size_manual(values = c(3,6)) +
  facet_wrap(.~Grass, scales = "free") +
  labs(y="") +
  ggtitle("", subtitle = "ITS - k=4") +
  theme_bw() + #default is 11
  theme(legend.position="right",
    axis.text.x = element_text(color="black", size=12),
    axis.text.y = element_text(color="black", size=12),
    axis.title.x = element_text(color="black", size=14),
    axis.title.y = element_text(color="black", size=14),
    plot.background = element_blank(),panel.border = element_rect(colour = "black", fill=NA, linewidth=.75),
    panel.grid.major = element_blank(),panel.grid.minor = element_blank(),panel.background = element_blank())

# facet Bin
plot_ordination(Fun_wholecommunity, Fun_wholecommunity.k3.ord, color = "Grass") +
  geom_text(mapping = aes(label = Site), size = 4.5, vjust = 1.5) +
  scale_color_manual(values = fun_grass_cols
                     # ,name="Grass Host"
                     ) +
  geom_point(size = 3, alpha = 0.9) +
  facet_wrap(.~Bin, scales = "free") +
  labs(y="") +
  ggtitle("", subtitle = "ITS - k=4") +
  theme_bw() +
  theme(legend.position="right",
    axis.text.x = element_text(color="black", size=12),
    axis.text.y = element_text(color="black", size=12),
    axis.title.x = element_text(color="black", size=14),
    axis.title.y = element_text(color="black", size=14),
    plot.background = element_blank(),panel.border = element_rect(colour = "black", fill=NA, linewidth=.75),
    panel.grid.major = element_blank(),panel.grid.minor = element_blank(),panel.background = element_blank())

# facet Bin*Gradient
plot_ordination(Fun_wholecommunity, Fun_wholecommunity.k3.ord, color = "Grass") +
  geom_text(mapping = aes(label = Site), size = 4.5, vjust = 1.5) +
  scale_color_manual(values = fun_grass_cols
                     # ,name="Grass Host"
                     ) +
  geom_point(size = 3, alpha = 0.9) +
  facet_wrap(.~Bin:Gradient) +
  labs(y="") +
  ggtitle("", subtitle = "ITS - k=4, scales=fixed") +
  theme_bw() +
  theme(legend.position="right",
    axis.text.x = element_text(color="black", size=12),
    axis.text.y = element_text(color="black", size=12),
    axis.title.x = element_text(color="black", size=14),
    axis.title.y = element_text(color="black", size=14),
    plot.background = element_blank(),panel.border = element_rect(colour = "black", fill=NA, linewidth=.75),
    panel.grid.major = element_blank(),panel.grid.minor = element_blank(),panel.background = element_blank())

plot_ordination(Fun_wholecommunity, Fun_wholecommunity.k3.ord, color = "Grass") +
  geom_text(mapping = aes(label = Site), size = 4, vjust = 1.5) +
  scale_color_manual(values = fun_grass_cols
                     # ,name="Grass Host"
                     ) +
  geom_point(size = 3, alpha = 0.9) +
  facet_wrap(.~Bin:Gradient, scales = "free") +
  labs(y="") +
  ggtitle("", subtitle = "ITS - k=4, scales=free") +
  theme_bw() +
  theme(legend.position="right",
    axis.text.x = element_text(color="black", size=12),
    axis.text.y = element_text(color="black", size=12),
    axis.title.x = element_text(color="black", size=14),
    axis.title.y = element_text(color="black", size=14),
    plot.background = element_blank(),panel.border = element_rect(colour = "black", fill=NA, linewidth=.75),
    panel.grid.major = element_blank(),panel.grid.minor = element_blank(),panel.background = element_blank())

adonis2(phyloseq::distance(Fun_wholecommunity, method="bray") ~ Site*Grass, 
          data = (as(sample_data(Fun_wholecommunity), "data.frame")))

anosim(phyloseq::distance(Fun_wholecommunity, method="bray"),
          (as(sample_data(Fun_wholecommunity), "data.frame"))$Site, permutations = 1000
       )

anosim(BC.dist, meta$AgeGroup, permutations = 1000)

3.8 long

plot_ordination(Fun_wholecommunity, Fun_wholecommunity.k3.ord, color = "Gradient", label = "Grass") +
  scale_color_manual(values = fun_grad_cols_n3
                     # ,name="Grass Host"
                     ) +
  geom_point(size = 3, alpha = 0.9) +
  labs(y="") +
  ggtitle("", subtitle = "ITS - k=4") +
  theme_bw() +
  theme(legend.position="right",
    axis.text.x = element_text(color="black", size=12),
    axis.text.y = element_text(color="black", size=12),
    axis.title.x = element_text(color="black", size=14),
    axis.title.y = element_text(color="black", size=14),
    plot.background = element_blank(),panel.border = element_rect(colour = "black", fill=NA, linewidth=.75),
    panel.grid.major = element_blank(),panel.grid.minor = element_blank(),panel.background = element_blank())

plot_ordination(Fun_wholecommunity, Fun_wholecommunity.k5.ord, color = "Gradient", label = "Grass") +
  scale_color_manual(values = fun_grad_cols_n3
                     # ,name="Grass Host"
                     ) +
  geom_point(size = 3, alpha = 0.9) +
  labs(y="") +
  ggtitle("", subtitle = "ITS - k=5") +
  theme_bw() +
  theme(legend.position="right",
    axis.text.x = element_text(color="black", size=12),
    axis.text.y = element_text(color="black", size=12),
    axis.title.x = element_text(color="black", size=14),
    axis.title.y = element_text(color="black", size=14),
    plot.background = element_blank(),panel.border = element_rect(colour = "black", fill=NA, linewidth=.75),
    panel.grid.major = element_blank(),panel.grid.minor = element_blank(),panel.background = element_blank())

plot_ordination(Fun_wholecommunity, Fun_wholecommunity.k3.ord, color = "Gradient", label = "Site") +
  scale_color_manual(values = fun_grad_cols_n3
                     # ,name="Grass Host"
                     ) +
  geom_point(size = 3, alpha = 0.9) +
  facet_wrap(.~Grass) +
  labs(y="") +
  ggtitle("", subtitle = "ITS - k=4") +
  theme_bw() +
  theme(legend.position="right",
    axis.text.x = element_text(color="black", size=12),
    axis.text.y = element_text(color="black", size=12),
    axis.title.x = element_text(color="black", size=14),
    axis.title.y = element_text(color="black", size=14),
    plot.background = element_blank(),panel.border = element_rect(colour = "black", fill=NA, linewidth=.75),
    panel.grid.major = element_blank(),panel.grid.minor = element_blank(),panel.background = element_blank())

plot_ordination(Fun_wholecommunity, Fun_wholecommunity.k5.ord, color = "Gradient", label = "Site") +
  scale_color_manual(values = fun_grad_cols_n3
                     # ,name="Grass Host"
                     ) +
  geom_point(size = 3, alpha = 0.9) +
  facet_wrap(.~Grass) +
  labs(y="") +
  ggtitle("", subtitle = "ITS - k=5") +
  theme_bw() +
  theme(legend.position="right",
    axis.text.x = element_text(color="black", size=12),
    axis.text.y = element_text(color="black", size=12),
    axis.title.x = element_text(color="black", size=14),
    axis.title.y = element_text(color="black", size=14),
    plot.background = element_blank(),panel.border = element_rect(colour = "black", fill=NA, linewidth=.75),
    panel.grid.major = element_blank(),panel.grid.minor = element_blank(),panel.background = element_blank())

3.9 lat

plot_ordination(Fun_wholecommunity, Fun_wholecommunity.k3.ord, color = "Bin", label = "Grass") +
  scale_color_manual(values = fun_bin_cols_n5
                     # ,name="Grass Host"
                     ) +
  geom_point(size = 3, alpha = 0.9) +
  labs(y="") +
  ggtitle("", subtitle = "ITS - k=4") +
  theme_bw() +
  theme(legend.position="right",
    axis.text.x = element_text(color="black", size=12),
    axis.text.y = element_text(color="black", size=12),
    axis.title.x = element_text(color="black", size=14),
    axis.title.y = element_text(color="black", size=14),
    plot.background = element_blank(),panel.border = element_rect(colour = "black", fill=NA, linewidth=.75),
    panel.grid.major = element_blank(),panel.grid.minor = element_blank(),panel.background = element_blank())

plot_ordination(Fun_wholecommunity, Fun_wholecommunity.k5.ord, color = "Bin", label = "Grass") +
  scale_color_manual(values = fun_bin_cols_n5
                     # ,name="Grass Host"
                     ) +
  geom_point(size = 3, alpha = 0.9) +
  labs(y="") +
  ggtitle("", subtitle = "ITS - k=5") +
  theme_bw() +
  theme(legend.position="right",
    axis.text.x = element_text(color="black", size=12),
    axis.text.y = element_text(color="black", size=12),
    axis.title.x = element_text(color="black", size=14),
    axis.title.y = element_text(color="black", size=14),
    plot.background = element_blank(),panel.border = element_rect(colour = "black", fill=NA, linewidth=.75),
    panel.grid.major = element_blank(),panel.grid.minor = element_blank(),panel.background = element_blank())

not working/old/bad

3.9.1 REDUNDANT Combining lat and long {.tabset .tabset-pills. hidden}

Coloring by both lat and long gradients

Map

on a plot